Factor Tearsheet¶
When implementing a factor in a trading algorithm, the complexity and wide range of parameters that come with basket selection and trading logic hinder our ability to evaluate the value factor's alpha signal in isolation. Before we proceed to the implementation of an algorithm, we want to know if the factor has any predictive value.
In this analysis, we'll measure a factor's predictive value using the Spearman rank correlation between the factor value and various N day forward price movement windows over a large universe of stocks. This correlation is called the Information Coefficient (IC). This tear sheet takes a pipeline factor and attempt to answer the following questions, in order:
- What is the sector-neutral rolling mean IC for our different forward price windows?
- What are the sector-neutral factor decile mean returns for our different forward price windows?
- How much are the contents of the top and bottom quintile changing each day?
- What is the autocorrelation in sector-wise factor rankings?
- What is IC decay (difference in IC for different forward price windows) for each sector?
- What is the IC decay for each sector over time?
- What are the factor quintile returns for each sector?
For more information on Spearman Rank correlation, check out this notebook from the Quantopian lecture series.
In the plots that are not disagregated by sector, sector adjustment has been applied to forward price movements. You can think of this sector adjustment as incorperating the assumption of a sector-netural portfolio constraint. If we are equally weighted in each sector, we'd want our factor to help us compare stocks within their own sectors. For example, if AAPL 5-day forward return is 0.1% and the mean 5-day forward return for the Technology stocks in our universe was 0.5% in the same period, the sector adjusted 5 day return for AAPL in this period is -0.4%.
The autocorrelation and decile turnover figures are meant to be used as a measure of factor horizon. It is worth noting that these stats are potentially misleading, as our top X liquidity constraint makes our universe dynamic. This dynamic universe likely contributes to a higher quantile turnover and lower rank autocorrelation than we would see in a static universe.
In [1]:
from __future__ import division
from quantopian.pipeline import Pipeline
from quantopian.pipeline import CustomFactor
from quantopian.research import run_pipeline
from quantopian.pipeline.data import morningstar
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import Latest, Returns, CustomFactor, AverageDollarVolume, SimpleMovingAverage
from quantopian.pipeline.filters.morningstar import IsPrimaryShare
import math
import datetime
import numpy as np
import pandas as pd
import scipy as sp
import pyfolio as pf
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
SECTOR_NAMES = {
101: 'Basic Materials',
102: 'Consumer Cyclical',
103: 'Financial Services',
104: 'Real Estate',
205: 'Consumer Defensive',
206: 'Healthcare',
207: 'Utilities',
308: 'Communication Services',
309: 'Energy',
310: 'Industrials',
311: 'Technology' ,
}
In [2]:
def _beta(stock_prices, bench_prices):
# `linregress` returns its results in the following order:
# slope, intercept, r-value, p-value, stderr
regr_results = stats.linregress(y=stock_prices, x=bench_prices)
#alpha = regr_results[1]
beta = regr_results[0]
#r_value = regr_results[2]
p_value = regr_results[3]
#stderr = regr_results[4]
# Check null hypothesis
if p_value > 0.05:
beta = 0.
return beta
def _beta_alternative(stock_prices, bench_prices):
m, b = np.polyfit(bench_prices, stock_prices, 1)
return m
class ReturnsBetaExcess(CustomFactor):
"""
Calculates the percent change in close price (beta adjusted) over the given window_length.
**Default Inputs**: [USEquityPricing.close]
"""
params = ('delta_days', 'market_sid',)
inputs = [USEquityPricing.close]
window_length = 90
def compute(self, today, assets, out, close, delta_days, market_sid):
returns = (close[delta_days:] - close[:-delta_days]) # absolute returns
returns /= close[:-delta_days] # percentage returns
market_idx = assets.get_loc(market_sid)
market_returns = returns[:,market_idx]
betas = np.apply_along_axis(_beta, 0, returns, market_returns)
returns -= (returns[:, [market_idx] ] * betas) # remove returns due to beta
out[:] = returns[-1]
# this is for comparison
class ReturnsMarketExcess(CustomFactor):
"""
Calculates the percent change in close price (market adjusted) over the given window_length.
**Default Inputs**: [USEquityPricing.close]
"""
params = ('market_sid',)
inputs = [USEquityPricing.close]
def compute(self, today, assets, out, close, market_sid):
returns = (close[-1] - close[0]) / close[0]
market_idx = assets.get_loc(market_sid)
returns -= returns[market_idx] # remove market returns
out[:] = returns
class SidFactor(CustomFactor):
"""
Workaround to screen by sids in pipeline
"""
inputs = []
window_length = 1
sids = [] # list, tuple and whatever np.asarray accept
def compute(self, today, assets, out):
out[:] = np.in1d(assets, self.sids)
def create_sid_screen(sids):
"""
Parameters
----------
sids : list of sids
List of securities to filter by
Returns
-------
Pipeline Filter that filters for the list of sids passed in
"""
sid_factor = SidFactor()
sid_factor.sids = sids
return sid_factor.eq(True)
def get_daily_perc_ret(sid_universe, start_date, end_date, ret_type='normal', market=None):
"""
Creates a DataFrame containing daily percentage returns: normal, market_excess or beta_excess returns
Parameters
----------
sid_universe : list
List of sids for which the ruturns are computed.
start_date : string
Starting date for returns computation.
end_date : string
End date for returns computation.
ret_type: string
Type of returns: normal, market_excess or beta_excess
market: equity
The market, if None is passed 'SPY' will be used
"""
if market is None:
market = symbols('SPY')
mask = create_sid_screen( sid_universe + [market.sid] )
inputs = [USEquityPricing.open]
daily_ret_columns = {
'normal': Returns(inputs=inputs, window_length=2, mask=mask),
'market_excess': ReturnsMarketExcess(inputs=inputs, window_length=2, market_sid=market.sid, mask=mask),
'beta_excess': ReturnsBetaExcess(inputs=inputs, window_length=60, mask=mask, delta_days=1, market_sid=market.sid),
}
tmp_pipe = Pipeline( columns={'daily_perc_ret':daily_ret_columns[ret_type]} )
tmp_pipe.set_screen(mask)
perc_ret_pipe = run_pipeline(tmp_pipe, start_date=start_date, end_date=end_date)
perc_ret_pipe = perc_ret_pipe.unstack().fillna(0)
perc_ret_pipe = perc_ret_pipe['daily_perc_ret'] #this drop top level column
# pipeline is run in the morning of each days before having open/close price for that day,
# this means we need to shift the return to have that day return
perc_ret_pipe = perc_ret_pipe.shift(-1)[:-1]
return perc_ret_pipe
In [3]:
class Sector(CustomFactor):
inputs = [morningstar.asset_classification.morningstar_sector_code]
window_length = 1
def compute(self, today, assets, out, msc):
out[:] = msc[-1]
def high_volume_universe(min_price = 0., min_volume = 0.):
"""
Computes a security universe based on nine different filters:
1. The security is common stock
2 & 3. It is not limited partnership - name and database check
4. The database has fundamental data on this stock
5. Not over the counter
6. Not when issued
7. Not depository receipts
8. Is Primary share
9. Has high dollar volume
Returns
-------
high_volume_tradable - zipline.pipeline.factor.Rank
A ranked AverageDollarVolume factor that's filtered on the nine criteria
"""
common_stock = morningstar.share_class_reference.security_type.latest.eq('ST00000001')
not_lp_name = ~morningstar.company_reference.standard_name.latest.matches('.* L[\\. ]?P\.?$')
not_lp_balance_sheet = morningstar.balance_sheet.limited_partnership.latest.isnull()
have_data = morningstar.valuation.market_cap.latest.notnull()
not_otc = ~morningstar.share_class_reference.exchange_id.latest.startswith('OTC')
not_wi = ~morningstar.share_class_reference.symbol.latest.endswith('.WI')
not_depository = ~morningstar.share_class_reference.is_depositary_receipt.latest
primary_share = IsPrimaryShare()
# Combine the above filters.
tradable_filter = (common_stock & not_lp_name & not_lp_balance_sheet &
have_data & not_otc & not_wi & not_depository & primary_share)
price = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=21, mask=tradable_filter)
volume = SimpleMovingAverage(inputs=[USEquityPricing.volume],
window_length=21, mask=tradable_filter)
full_filter = tradable_filter & (price >= min_price) & (volume >= min_volume)
high_volume_tradable = AverageDollarVolume(
window_length=21,
mask=full_filter
).rank(ascending=False)
return high_volume_tradable
def construct_factor_history(factor_cls, start_date='2015-10-1', end_date='2016-2-1',
factor_name='factor',
top_liquid=1000, universe_constraints=None, sector_names=None):
"""
Creates a DataFrame containing daily factor values and sector codes for a liquidity
constrained universe. The returned DataFrame is can be used in the factor tear sheet.
Parameters
----------
factor_cls : quantopian.pipeline.CustomFactor
Factor class to be computed.
start_date : string or pd.datetime
Starting date for factor computation.
end_date : string or pd.datetime
End date for factor computation.
factor_name : string, optional
Column name for factor column in returned DataFrame.
top_liquid : int, optional
Limit universe to the top N most liquid names each trading day.
Based on trailing 5 days traded dollar volume.
universe_constraints : num_expr, optional
Pipeline universe constraint.
Returns
-------
daily_factor : pd.DataFrame
DataFrame with integer index and date, equity, factor, and sector
code columns.
"""
ok_universe = (high_volume_universe() < top_liquid)
if universe_constraints is not None:
ok_universe = ok_universe & universe_constraints
factor = factor_cls(mask=ok_universe)
sector = Sector(mask=ok_universe)
ok_universe = ok_universe & factor.eq(factor) & sector.eq(sector)
pipe = Pipeline()
pipe.add(factor, factor_name)
pipe.add(sector, 'sector_code')
pipe.set_screen(ok_universe)
daily_factor = run_pipeline(pipe, start_date=start_date, end_date=end_date)
daily_factor = daily_factor.reset_index().rename(
columns={'level_0': 'date', 'level_1':'equity'})
daily_factor = daily_factor[daily_factor.sector_code != -1]
if sector_names is not None:
daily_factor.sector_code = daily_factor.sector_code.apply(
lambda x: sector_names[x])
return daily_factor
def get_daily_returns(daily_factor, start_date, end_date, extra_days_before=0, extra_days_after=0, ret_type='normal'):
"""
Creates a DataFrame containing daily percentage returns: normal, market_excess or beta_excess returns
Parameters
----------
daily_factor : pd.DataFrame
DataFrame with, at minimum, date, equity, factor, columns. Index can be integer or
date/equity multiIndex.
See construct_factor_history for more detail.
start_date : string
Starting date for returns computation.
end_date : string
End date for returns computation.
ret_type: string
Type of returns: normal, market_excess or beta_excess
Returns
-------
price : pd.DataFrame
DataFrame with date index and equity columns with and percentage price movement that can be
normal, market excess or beta excess
"""
extra_days = math.ceil(extra_days_before * 365.0/252.0) + 5 # just to be sure
start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d") - datetime.timedelta(days=extra_days)
start_date = start_date.strftime("%Y-%m-%d")
extra_days = math.ceil(extra_days_after * 365.0/252.0) + 5 # just to be sure
end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d") + datetime.timedelta(days=extra_days)
end_date = end_date.strftime("%Y-%m-%d")
sid_universe = daily_factor['equity'].unique()
sid_universe = map(lambda x:x.sid, sid_universe)
daily_perc_ret = get_daily_perc_ret(sid_universe=sid_universe, start_date=start_date, end_date=end_date,
ret_type=ret_type)
return daily_perc_ret
def add_forward_price_movement(daily_factor, days=[1, 5, 10], prices=None):
"""
Adds N day forward price movements (as percent change) to a factor value
DataFrame.
Parameters
----------
daily_factor : pd.DataFrame
DataFrame with, at minimum, date, equity, factor, columns. Index can be integer or
date/equity multiIndex.
See construct_factor_history for more detail.
days : list
Number of days forward to project price movement. One column will be added for each value.
prices : pd.DataFrame, optional
Pricing data to use in forward price calculation. Equities as columns, dates as index.
If no value is passed, get pricing will be called.
Returns
-------
factor_and_fp : pd.DataFrame
DataFrame with integer index and date, equity, factor, sector
code columns with and an arbitary number of N day forward percentage
price movement columns.
"""
factor_and_fp = daily_factor.copy()
if not isinstance(factor_and_fp.index, pd.core.index.MultiIndex):
factor_and_fp = factor_and_fp.set_index(['date', 'equity'])
if prices is None:
start_date = factor_and_fp.index.levels[0].values.min()
end_date = factor_and_fp.index.levels[0].values.max()
equities = factor_and_fp.index.levels[1].unique()
time_buffer = pd.Timedelta(days=max(days)+5)
prices = get_pricing(equities,
start_date=start_date,
end_date=end_date+time_buffer,
fields='open_price')
col_n = '%s_day_fwd_price_change'
for i in days:
delta = prices.pct_change(i).shift(-i)
factor_and_fp[col_n%i] = delta.stack()
factor_and_fp = factor_and_fp.reset_index()
return factor_and_fp
def sector_adjust_forward_price_moves(factor_and_fp):
"""
Convert forward price movements to price movements relative to mean sector price movements.
This normalization incorperates the assumption of a sector neutral portfolio constraint
and thus allows allows the factor to be evaluated across sectors.
For example, if AAPL 5 day return is 0.1% and mean 5 day return for the Technology stocks
in our universe was 0.5% in the same period, the sector adjusted 5 day return for AAPL
in this period is -0.4%.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns. Index should be integer.
See add_forward_price_movement for more detail.
Returns
-------
adj_factor_and_fp : pd.DataFrame
DataFrame with integer index and date, equity, factor, sector
code columns with and an arbitary number of N day forward percentage
price movement columns, each normalized by sector.
"""
adj_factor_and_fp = factor_and_fp.copy()
pc_cols = [col for col in factor_and_fp.columns.values if 'fwd_price_change' in col]
adj_factor_and_fp[pc_cols] = factor_and_fp.groupby(['date', 'sector_code'])[pc_cols].apply(
lambda x: x - x.mean())
return adj_factor_and_fp
def factor_spearman_rank_IC(factor_and_fp, time_rule=None, by_sector=True, factor_name='factor'):
"""
Computes sector neutral Spearman Rank Correlation based Information Coefficient between
factor values and N day forward price movements.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns. Index should be integer.
See add_forward_price_movement for more detail.
time_rule : string, optional
Time span to use in Pandas DateTimeIndex grouping reduction.
See http://pandas.pydata.org/pandas-docs/stable/timeseries.html for available options.
by_sector : boolean
If True, compute ic separately for each sector
factor_name : string
Name of factor column on which to compute IC.
Returns
-------
ic : pd.DataFrame
Spearman Rank correlation between factor and provided forward price movement columns.
MultiIndex of date, sector.
err : pd.DataFrame
Standard error of computed IC. MultiIndex of date, sector.
MultiIndex of date, sector.
"""
def src_ic(x):
cn = "%s_day_IC"
ic = pd.Series()
for days, col in zip(fwd_days, pc_cols):
ic[cn%days] = sp.stats.spearmanr(x[factor_name], x[col])[0]
ic['obs_count'] = len(x)
return ic
def src_std_error(rho, n):
return np.sqrt((1-rho**2)/(n-2))
fwd_days, pc_cols = get_price_move_cols(factor_and_fp)
grpr = ['date', 'sector_code'] if by_sector else ['date']
ic = factor_and_fp.groupby(grpr).apply(src_ic)
obs_count = ic.pop('obs_count')
err = ic.apply(lambda x: src_std_error(x, obs_count))
if time_rule is not None:
ic = ic.reset_index().set_index('date')
err = err.reset_index().set_index('date')
grpr = [pd.TimeGrouper(time_rule),'sector_code'] if by_sector else [pd.TimeGrouper(time_rule)]
ic = ic.groupby(grpr).mean()
err = err.groupby(grpr).agg(
lambda x: np.sqrt((np.sum(np.power(x, 2))/len(x))))
else:
if by_sector:
ic = ic.reset_index().groupby(['sector_code']).mean()
err = err.reset_index().groupby(['sector_code']).agg(
lambda x: np.sqrt((np.sum(np.power(x, 2))/len(x))))
return ic, err
def quantile_bucket_factor(factor_and_fp, by_sector=True, quantiles=5, factor_name='factor'):
"""
Computes daily factor quantiles.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns.
Index should be integer. See add_forward_price_movement for more detail.
by_sector : boolean
If True, compute quantile buckets separately for each sector.
quantiles : integer
Number of quantiles buckets to use in factor bucketing.
factor_name : string
Name of factor column on which to compute quantiles.
Returns
-------
factor_and_fp_ : pd.DataFrame
Factor and forward price movements with additional factor quantile column.
"""
g_by = ['date', 'sector_code'] if by_sector else ['date']
factor_and_fp_ = factor_and_fp.copy()
factor_and_fp_['factor_percentile'] = factor_and_fp_.groupby(
g_by)[factor_name].rank(pct=True)
q_int_width = 1. / quantiles
factor_and_fp_['factor_bucket'] = factor_and_fp_.factor_percentile.apply(
lambda x: ((x - .000000001) // q_int_width) + 1)
return factor_and_fp_
def quantile_bucket_mean_daily_return(quantile_factor, by_sector=False):
"""
Computes mean daily returns for factor quantiles across provided forward
price movement columns.
Parameters
----------
quantile_factor : pd.DataFrame
DataFrame with date, equity, factor, factor quantile, and forward price movement columns.
Index should be integer. See quantile_bucket_factor for more detail.
by_sector : boolean
If True, compute quintile bucket returns separately for each sector
quantiles : integer
Number of quantiles buckets to use in factor bucketing.
Returns
-------
mean_returns_by_quantile : pd.DataFrame
Sector-wise mean daily returns by specified factor quantile.
"""
fwd_days, pc_cols = get_price_move_cols(quantile_factor)
def daily_mean_ret(x):
mean_ret = pd.Series()
for days, col in zip(fwd_days, pc_cols):
mean_ret[col] = x[col].mean() / days
return mean_ret
g_by = ['sector_code', 'factor_bucket'] if by_sector else ['factor_bucket']
mean_ret_by_quantile = quantile_factor.groupby(
g_by)[pc_cols].apply(daily_mean_ret)
return mean_ret_by_quantile
def quantile_turnover(quantile_factor, quantile):
"""
Computes the proportion of names in a factor quantile that were
not in that quantile in the previous period.
Parameters
----------
quantile_factor : pd.DataFrame
DataFrame with date, equity, factor, factor quantile, and forward price movement columns.
Index should be integer. See quantile_bucket_factor for more detail.
quantile : integer
Quantile on which to perform turnover analysis.
Returns
-------
quant_turnover : pd.Series
Period by period turnover for that quantile.
"""
quant_names = quantile_factor[quantile_factor.factor_bucket == quantile]
quant_name_sets = quant_names.groupby(['date']).equity.apply(set)
new_names = (quant_name_sets - quant_name_sets.shift(1)).dropna()
quant_turnover = new_names.apply(lambda x: len(x)) / quant_name_sets.apply(lambda x: len(x))
return quant_turnover
def factor_rank_autocorrelation(daily_factor, time_rule='W', factor_name='factor'):
"""
Computes autocorrelation of mean factor ranks in specified timespans.
We must compare week to week factor ranks rather than factor values to account for
systematic shifts in the factor values of all names or names within a sector.
This metric is useful for measuring the turnover of a factor. If the value of a factor
for each name changes randomly from week to week, we'd expect a weekly autocorrelation of 0.
Parameters
----------
daily_factor : pd.DataFrame
DataFrame with integer index and date, equity, factor, and sector
code columns.
time_rule : string, optional
Time span to use in factor grouping mean reduction.
See http://pandas.pydata.org/pandas-docs/stable/timeseries.html for available options.
factor_name : string
Name of factor column on which to compute IC.
Returns
-------
autocorr : pd.Series
Rolling 1 period (defined by time_rule) autocorrelation of factor values.
"""
daily_ranks = daily_factor.copy()
daily_ranks[factor_name] = daily_factor.groupby(['date', 'sector_code'])[factor_name].apply(
lambda x: x.rank(ascending=True))
equity_factor = daily_ranks.pivot(index='date', columns='equity', values=factor_name)
if time_rule is not None:
equity_factor = equity_factor.resample(time_rule, how='mean')
autocorr = equity_factor.corrwith(equity_factor.shift(1), axis=1)
return autocorr
def get_price_move_cols(x):
pc_cols = [col for col in x.columns.values if 'fwd_price_change' in col]
fwd_days = map(lambda x: int(x.split('_')[0]), pc_cols)
return fwd_days, pc_cols
def get_ic_cols(x):
return [col for col in x.columns.values if 'day_IC' in col]
def is_outlier(points, thresh=3.0):
"""
Utility function to remove outliers, for a better graph visualization
Returns a boolean array with True if points are outliers and False
otherwise.
Parameters:
-----------
points : An numobservations by numdimensions array of observations
thresh : The modified z-score to use as a threshold. Observations with
a modified z-score (based on the median absolute deviation) greater
than this value will be classified as outliers.
Returns:
--------
mask : A numobservations-length boolean array.
References:
----------
Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
Handle Outliers", The ASQC Basic References in Quality Control:
Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.
"""
if len(points.shape) == 1:
points = points[:,None]
median = np.median(points, axis=0)
diff = np.sum((points - median)**2, axis=-1)
diff = np.sqrt(diff)
med_abs_deviation = np.median(diff)
modified_z_score = 0.6745 * diff / med_abs_deviation
return modified_z_score > thresh
def build_cumulative_returns_series(factor_and_fp, daily_perc_ret, days_before, days_after, day_zero_align=False):
"""
An equity and date pair is extracted from each row in the input dataframe and for each of
these pairs a cumulative return time series is built starting 'days_before' days
before and ending 'days_after' days after the date specified in the pair
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with at least date and equity columns.
daily_perc_ret : pd.DataFrame
Pricing data to use in cumulative return calculation. Equities as columns, dates as index.
day_zero_align : boolean
Aling returns at day 0 (timeseries is 0 at day 0)
"""
ret_df = pd.DataFrame()
for index, row in factor_and_fp.iterrows():
timestamp, equity = row['date'], row['equity']
timestamp_idx = daily_perc_ret.index.get_loc(timestamp)
start = timestamp_idx - days_before
end = timestamp_idx + days_after
series = daily_perc_ret.ix[start:end, equity]
ret_df = pd.concat( [ret_df, series], axis=1, ignore_index=True)
# Reset index to have the same starting point (from datetime to day offset)
ret_df = ret_df.apply(lambda x : x.dropna().reset_index(drop=True), axis=0)
ret_df.index = range(-days_before, days_after)
# From daily percent returns to comulative returns
ret_df = (ret_df + 1).cumprod() - 1
# Make returns be 0 at day 0
if day_zero_align:
ret_df -= ret_df.iloc[days_before,:]
return ret_df
In [4]:
def plot_daily_ic(factor_and_fp, factor_name='factor'):
"""
Plots Spearman Rank Information Coefficient and IC moving average for a given factor.
Sector neturalization of forward price movements with sector_adjust_forward_price_moves is
recommended.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns.
factor_name : string
Name of factor column on which to compute IC.
"""
daily_ic, _ = factor_spearman_rank_IC(factor_and_fp, by_sector=False,
factor_name=factor_name)
ic_cols = get_ic_cols(daily_ic)
for col in ic_cols:
mean_ic = daily_ic[col].mean()
std_ic = daily_ic[col].std()
fp_ic = pd.DataFrame(daily_ic[col])
fp_ic['1 month moving avg'] = pd.rolling_mean(fp_ic[col], 22)
fp_ic.plot(title= "{} {} (sector adjusted)".format(factor_name, col), figsize=(20,10))
print('{} mean: {}'.format(col, mean_ic))
print('{} stdev: {}'.format(col, std_ic))
print('{} mean/stdev: {}'.format(col, mean_ic/std_ic))
plt.ylabel('IC')
plt.xlabel('date')
plt.show()
sns.distplot(daily_ic[col].replace(np.nan, 0) ,norm_hist=True)
plt.show()
def plot_ic_by_sector(factor_and_fp, factor_name='factor'):
"""
Plots Spearman Rank Information Coefficient for a given factor over provided forward price
movement windows. Separates by sector.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns.
factor_name : string
Name of factor column on which to compute IC.
"""
ic_sector, err_sector = factor_spearman_rank_IC(factor_and_fp, factor_name=factor_name)
ic_sector.plot(kind='bar' ) #yerr=err_sector
fig = plt.gcf()
fig.suptitle("Information Coefficient by Sector", fontsize=16, x=.5, y=.93)
plt.show()
def plot_ic_by_sector_over_time(factor_and_fp, time_rule=None, factor_name='factor'):
"""
Plots sector-wise time window mean daily Spearman Rank Information Coefficient
for a given factor over provided forward price movement windows.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns.
time_rule : string, optional
Time span to use in time grouping reduction.
See http://pandas.pydata.org/pandas-docs/stable/timeseries.html for available options.
factor_name : string
Name of factor column on which to compute IC.
"""
ic_time, err_time = factor_spearman_rank_IC(factor_and_fp, time_rule=time_rule,
factor_name=factor_name)
ic_time = ic_time.reset_index()
err_time = err_time.reset_index()
f, axes = plt.subplots(6,2, sharex=False, sharey=True, figsize=(20,45))
axes = axes.flatten()
i = 0
for sc, data in ic_time.groupby(['sector_code']):
e = err_time[err_time.sector_code == sc].set_index('date')
data.drop('sector_code', axis=1).set_index('date').plot(kind='bar',
title=sc,
ax=axes[i],
) # yerr=e
i+=1
fig = plt.gcf()
fig.suptitle("Monthly Information Coefficient by Sector", fontsize=16, x=.5, y=.93)
plt.show()
def plot_quantile_returns(factor_and_fp, by_sector=True, quantiles=5, factor_name='factor'):
"""
Plots sector-wise mean daily returns for factor quantiles
across provided forward price movement columns.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns.
by_sector : boolean
Disagregate figures by sector.
quantiles : integer
Number of quantiles buckets to use in factor bucketing.
factor_name : string
Name of factor column on which to compute IC.
"""
decile_factor = quantile_bucket_factor(factor_and_fp, by_sector=by_sector, quantiles=quantiles,
factor_name=factor_name)
mean_ret_by_q = quantile_bucket_mean_daily_return(decile_factor, by_sector=by_sector)
if by_sector:
f, axes = plt.subplots(6,2, sharex=False, sharey=True, figsize=(20,45))
axes = axes.flatten()
i = 0
for sc, cor in mean_ret_by_q.groupby(level='sector_code'):
cor = cor.reset_index().drop('sector_code', axis=1).set_index('factor_bucket')
cor.plot(kind='bar', title=sc, ax=axes[i])
axes[i].set_xlabel('factor quantile')
axes[i].set_ylabel('mean daily price % change')
i+=1
fig = plt.gcf()
fig.suptitle(factor_name + ":Daily Mean Return By Factor Quantile", fontsize=24, x=.5, y=.93)
else:
mean_ret_by_q.plot(kind='bar',
title="Daily Mean Return By Factor Quantile (sector adjusted)")
plt.xlabel('factor quantile')
plt.ylabel('mean daily price % change')
plt.show()
def plot_quantile_returns_box(factor_and_fp, by_sector=True, quantiles=5, factor_name='factor'):
"""
Plots sector-wise mean daily returns as boxplot for factor quantiles
across provided forward price movement columns.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns.
by_sector : boolean
Disagregate figures by sector.
quantiles : integer
Number of quantiles buckets to use in factor bucketing.
factor_name : string
Name of factor column on which to compute IC.
"""
decile_factor = quantile_bucket_factor(factor_and_fp, by_sector=by_sector, quantiles=quantiles,
factor_name=factor_name)
fwd_days, pc_cols = get_price_move_cols(decile_factor)
if by_sector:
f, axes = plt.subplots(6,2, sharex=False, sharey=True, figsize=(20,45))
axes = axes.flatten()
i = 0
for sc, cor in decile_factor.groupby(by='sector_code'):
cor_box_plot = pd.melt(cor, var_name='fwd_days_price', value_name='%_price_change',
id_vars=['factor_bucket'], value_vars=pc_cols)
# boxplot doesn't sort 'x' by itself
cor_box_plot = cor_box_plot.sort(columns='factor_bucket', ascending=True)
sns.boxplot(ax=axes[i], x="factor_bucket", y="%_price_change", hue="fwd_days_price",
hue_order=pc_cols, data=cor_box_plot)
axes[i].set_xlabel('factor quantile')
axes[i].set_ylabel('mean price % change')
axes[i].set_title(sc)
i+=1
fig = plt.gcf()
fig.suptitle(factor_name + ": Mean Return By Factor Quantile", fontsize=24, x=.5, y=.93)
else:
decile_factor_box_plot = pd.melt(decile_factor, var_name='fwd_days_price', value_name='%_price_change',
id_vars=['factor_bucket'], value_vars=pc_cols)
# boxplot doesn't sort 'x' by itself
decile_factor_box_plot = decile_factor_box_plot.sort(columns='factor_bucket', ascending=True)
sns.boxplot(x="factor_bucket", y="%_price_change", hue="fwd_days_price",
hue_order=pc_cols, data=decile_factor_box_plot)
plt.title("Mean Return By Factor Quantile (sector adjusted)")
plt.xlabel('factor quantile')
plt.ylabel('mean price % change')
plt.show()
def plot_factor_rank_auto_correlation(daily_factor, time_rule='W', factor_name='factor'):
"""
Plots factor rank autocorrelation over time. See factor_rank_autocorrelation for more details.
Parameters
----------
daily_factor : pd.DataFrame
DataFrame with date, equity, and factor value columns.
time_rule : string, optional
Time span to use in time grouping reduction prior to autocorrelation calculation.
See http://pandas.pydata.org/pandas-docs/stable/timeseries.html for available options.
factor_name : string
Name of factor column on which to compute IC.
"""
fa = factor_rank_autocorrelation(daily_factor, time_rule=time_rule, factor_name=factor_name)
print "Mean rank autocorrelation: " + str(fa.mean())
fa.plot(title='Week-to-Week Factor Rank Autocorrelation')
plt.ylabel('autocorrelation coefficient')
plt.show()
def plot_top_bottom_quantile_turnover(daily_factor, num_quantiles=5, factor_name='factor'):
"""
Plots daily top and bottom quantile factor turnover. See quantile_bucket_factor for more
details.
Parameters
----------
daily_factor : pd.DataFrame
DataFrame with date, equity, and factor value columns.
num_quantiles : integer
Number of quantiles to use in quantile bucketing.
factor_name : string
Name of factor column on which to compute IC.
"""
quint_buckets = quantile_bucket_factor(daily_factor, by_sector=True,
quantiles=5, factor_name=factor_name)
turnover = pd.DataFrame()
turnover['top quintile turnover'] = quantile_turnover(quint_buckets, num_quantiles)
turnover['bottom quintile turnover'] = quantile_turnover(quint_buckets, 1)
turnover.plot(title='Top and Bottom Quintile Turnover (Quantiles Computed by Sector)')
plt.ylabel('proportion of names not present in quantile in previous period')
plt.show()
def plot_factor_vs_fwdprice_distribution(factor_and_fp, factor_name='factor', remove_outliers = False):
"""
Plots distribuion of factor vs forward price.
This is useful to visually spot linear or non linear relationship between factor and fwd prices
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns.
factor_name : string
Name of factor column
remove_outliers : boolean
Remove outliers before plotting the distribution
"""
fwd_days, pc_cols = get_price_move_cols(factor_and_fp)
for col in pc_cols:
if remove_outliers:
data = factor_and_fp[ ~is_outlier(factor_and_fp[factor_name].values) & \
~is_outlier(factor_and_fp[col].values)]
else:
data = factor_and_fp
jg = sns.jointplot(data[factor_name], data[col], kind="kde")
jg.fig.suptitle('Factor/returns kernel density estimation' +
(' NO OUTLIERS' if remove_outliers else '') )
plt.show()
jg = sns.jointplot(data[factor_name], data[col], kind="reg")
jg.fig.suptitle('Factor/returns regression' +
(' NO OUTLIERS' if remove_outliers else '') )
plt.show()
def plot_quantile_cumulative_return(factor_and_fp, daily_perc_ret, quantiles=5, by_quantile = False,
factor_name='factor', days_before = 15, days_after = 15,
std_bar = True, day_zero_align = True):
"""
Plots sector-wise mean daily returns for factor quantiles
across provided forward price movement columns.
Parameters
----------
factor_and_fp : pd.DataFrame
DataFrame with date, equity, factor, and forward price movement columns.
daily_perc_ret : pd.DataFrame
Pricing data to use in cumulative return calculation. Equities as columns, dates as index.
quantiles : integer
Number of quantiles buckets to use in factor bucketing.
by_quantile : boolean
Disagregate figures by quantile.
factor_name : string
Name of factor column on which to compute IC.
days_before : int
How many days to plot before the factor is calculated
days_after : int
How many days to plot after the factor is calculated
day_zero_align : boolean
Aling returns at day 0 (timeseries is 0 at day 0)
std_bar : boolean
Plot standard deviation plot
"""
decile_factor = quantile_bucket_factor(factor_and_fp, by_sector=False, quantiles=quantiles,
factor_name=factor_name)
cumulative_returns = {}
for fb, cor in decile_factor.groupby(by='factor_bucket'):
cumulative_returns[fb] = build_cumulative_returns_series(cor, daily_perc_ret, days_before, days_after, day_zero_align)
palette = sns.color_palette("coolwarm", len(cumulative_returns))
if by_quantile:
nrows=int(round(len(cumulative_returns)/2.0))
ncols=2
fig, axes = plt.subplots(nrows, ncols, figsize=(20,45))
axes = axes.flatten()
i = 0
for fb, ret_df in cumulative_returns.iteritems():
# plot cumulative returs
label = 'Quantile ' + str(fb)
sns.tsplot(ax=axes[i], data=ret_df.T.values, condition=label, legend=True, color=palette[i], time=ret_df.index)
# , err_style="unit_traces") for single traces
axes[i].set_ylabel('% return')
# plot std dev bars
if std_bar:
mean = ret_df.mean(axis=1)
std = ret_df.std(axis=1)
axes[i].errorbar(ret_df.index, mean, yerr=std, fmt='-o', color=palette[i])
# mark day zero with a vertical line
axes[i].axvline(x=0, color='k', linestyle='--')
i+=1
fig = plt.gcf()
fig.suptitle("Cumulative returns by quantile", fontsize=24, x=.5, y=.93)
else:
# plot cumulative returs
ax = None
i = 0
for fb, ret_df in cumulative_returns.iteritems():
label = 'Quantile ' + str(fb)
ax = sns.tsplot(ax=ax, data=ret_df.T.values, condition=label, legend=True, color=palette[i], time=ret_df.index)
# , err_style="unit_traces") for single traces
# plot std dev bars
if std_bar:
mean = ret_df.mean(axis=1)
std = ret_df.std(axis=1)
ax.errorbar(ret_df.index, mean, yerr=std, fmt='-o', color=palette[i])
i+=1
# mark day zero with a vertical line
ax.axvline(x=0, color='k', linestyle='--')
plt.xlabel('Days')
plt.ylabel('% return')
plt.title("Cumulative returns by quantile")
plt.show()
In [5]:
def create_factor_tear_sheet(factor_cls,
factor_name='factor',
start_date='2015-10-1',
end_date='2016-2-1',
top_liquid=1000,
sector_names=None,
days=[1, 5, 10],
nquantiles = 10,
ret_type='normal' # normal, market_excess or beta_excess
):
# number of days to plot before and after each factor
days_before=5
days_after=max(days)+3
factor = construct_factor_history(factor_cls, start_date=start_date, end_date=end_date,
factor_name=factor_name, top_liquid=top_liquid,
sector_names=sector_names)
daily_perc_ret = get_daily_returns(factor, start_date, end_date, extra_days_before=days_before,
extra_days_after=days_after, ret_type=ret_type)
#
# Pass comulative returns to add_forward_price_movement, in this way we can possible
# use market excess returns or beta excess returns for the full factor analysis
#
factor_and_fp = add_forward_price_movement(factor, days=days, prices=(daily_perc_ret+1.0).cumprod())
adj_factor_and_fp = sector_adjust_forward_price_moves(factor_and_fp)
# What is the sector-netural rolling mean IC for our different forward price windows?
plot_daily_ic(adj_factor_and_fp, factor_name=factor_name)
# Plot comulative returns over time for each quantile
plot_quantile_cumulative_return(factor_and_fp, daily_perc_ret, quantiles=nquantiles, by_quantile=False,
factor_name=factor_name, days_before=days_before, days_after=days_after, std_bar=False)
# Plot comulative returns over time, one plot for each quantile
plot_quantile_cumulative_return(factor_and_fp, daily_perc_ret, quantiles=nquantiles, by_quantile=True,
factor_name=factor_name, days_before=days_before, days_after=days_after, std_bar=True)
# What are the sector-neutral factor decile mean returns for our different forward price windows?
plot_quantile_returns(adj_factor_and_fp, by_sector=False, quantiles=nquantiles, factor_name=factor_name)
# As above but more detailed, we want to know the volatility of returns
plot_quantile_returns_box(adj_factor_and_fp, by_sector=False, quantiles=nquantiles, factor_name=factor_name)
# let's have a look at the relationship between factor and returns
plot_factor_vs_fwdprice_distribution(adj_factor_and_fp, factor_name=factor_name)
plot_factor_vs_fwdprice_distribution(adj_factor_and_fp, factor_name=factor_name, remove_outliers=True)
# How much is the contents of the the top and bottom quintile changing each day?
plot_top_bottom_quantile_turnover(factor, num_quantiles=5, factor_name=factor_name)
# What is the autocorrelation in factor rank? Should this be autocorrelation in sector-neutralized
# factor value?
plot_factor_rank_auto_correlation(factor, factor_name=factor_name)
# What is IC decay for each sector?
plot_ic_by_sector(factor_and_fp, factor_name=factor_name)
if pd.to_datetime(end_date) - pd.to_datetime(start_date) > pd.Timedelta(days=70):
tr = 'M'
else:
tr = 'W'
# What is the IC decay for each sector over time, not assuming sector neturality?
plot_ic_by_sector_over_time(adj_factor_and_fp, time_rule=tr, factor_name=factor_name)
# What are the factor quintile returns for each sector, not assuming sector neutrality?
plot_quantile_returns(adj_factor_and_fp, by_sector=True, quantiles=5, factor_name=factor_name)
# As above but more detailed, we want to know the volatility of returns
plot_quantile_returns_box(adj_factor_and_fp, by_sector=True, quantiles=5, factor_name=factor_name)
Factor Definition¶
Start by defining a pipeline factor.
Here we'll start by looking at free cash flow yield (free cash flow / market cap)
In [6]:
class FCFY(CustomFactor):
inputs = [morningstar.cash_flow_statement.free_cash_flow,
morningstar.valuation.market_cap]
window_length = 1
def compute(self, today, assets, out, fcf, mc):
out[:] = fcf[-1] / mc[-1]
Tearsheet¶
In [7]:
create_factor_tear_sheet(FCFY, factor_name='Free_Cash_Flow_Yield',
start_date='2014-1-1', end_date='2014-3-1',
top_liquid=500, sector_names=SECTOR_NAMES, days=[5, 10, 20])
EBITA/EV Tear Sheet¶¶
In [8]:
class EBITDA_to_EV(CustomFactor):
inputs = [morningstar.income_statement.ebitda,
morningstar.valuation.enterprise_value]
window_length = 1
def compute(self, today, assets, out, ebitda, ev):
out[:] = ebitda[-1] / ev[-1]
create_factor_tear_sheet(EBITDA_to_EV, factor_name='EBIDTA_to_EV',
start_date='2014-1-1', end_date='2014-3-1',
top_liquid=500, sector_names=SECTOR_NAMES, days=[5, 10, 20])
In [9]:
factor = construct_factor_history(EBITDA_to_EV, factor_name='EBIDTA_to_EV',
start_date='2014-1-1', end_date='2014-3-1',
top_liquid=500, sector_names=SECTOR_NAMES)
factor_and_fp = add_forward_price_movement(factor)
adj_factor_and_fp = sector_adjust_forward_price_moves(factor_and_fp)
In [10]:
plot_quantile_returns(adj_factor_and_fp, by_sector=False, quantiles=10, factor_name='EBIDTA_to_EV')
